scholarly journals FEM×DEM multiscale modeling: Model performance enhancement from Newton strategy to element loop parallelization

2018 ◽  
Vol 114 (1) ◽  
pp. 47-65 ◽  
Author(s):  
A. Argilaga ◽  
J. Desrues ◽  
S. Dal Pont ◽  
G. Combe ◽  
D. Caillerie
Keyword(s):  
Multiscale Modeling ◽  
Performance Enhancement ◽  
Model Performance ◽  
Loop Parallelization

Performance Enhancement of Pneumatic Vibration Isolators Using Self-Tuning PID Feedback and Time-Delay-Control (TDC)-Based Feedforward Scheme

2018 ◽  
Author(s):  
Jin-Wei Liang ◽  
Hung-Yi Chen ◽  
Lyu-Cyuan Zeng
Keyword(s):  
Mathematical Model ◽  
Performance Enhancement ◽  
Control Method ◽  
Hybrid Control ◽  
Model Performance ◽  
Experimental Investigations ◽  
Isolation System ◽  
Control Scheme ◽  
Self Tuning ◽  
Hybrid Control Scheme

A hybrid control scheme that combines a self-tuning PID-feedback loop and TDC-based feedforward scheme is proposed in this study to cope with an active pneumatic vibration isolator. In order to establish an effective TDC feedforward control a reliable mathematical model of the pneumatic isolator is required and developed firstly. Numerical and experimental investigations on the validity of the mathematical model are performed. It is found that although slight discrepancy exists between predicted and observed behaviors of the system, the overall model performance is acceptable. The resultant model is then applied in the design of the TDC feedforward scheme. A neuro-based adaptive PID control is integrated with the TDC feedforward algorithm to form the hybrid control. Numerical and experimental isolation tests are carried out to examine the suppression performances of the proposed hybrid control scheme. The results show that the proposed hybrid control method outperforms solely TDC feedforward while the latter outperforms the passive isolation system. Moreover, the proposed hybrid control scheme can suppress the vibration near the system’s resonance.

scholarly journals Arrhythmia Detection using Deep Belief Network Extracted Features from ECG Signals

2021 ◽  
Vol 12 (6) ◽  
pp. 0-0
Keyword(s):  
Feature Extraction ◽  
Performance Enhancement ◽  
Model Performance ◽  
Deep Belief Network ◽  
Arrhythmia Detection ◽  
Belief Network ◽  
Ecg Signals ◽  
Increase In Accuracy ◽  
Simple Logistic ◽  
Electrocardiogram Ecg

Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple Logistic Regression (LR) classifier. We compare and evaluate the results of data feature enrichment against plain, non-enriched data based on the metrics of precision, recall, specificity, and F1-score and report the extent of increase in performance. Also, we compare the performance of the DBN-LR pipeline with a 1D convolution technique and find that the DBN-LR algorithm achieves a 5% and 10% increase in accuracy when compared to 1D convolution and no feature extraction using DBN respectively.

scholarly journals Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals

2021 ◽  
Vol 12 (6) ◽  
pp. 1-24
Author(s):  
Mahendra Kumar Gourisaria ◽  
Harshvardhan GM ◽  
Rakshit Agrawal ◽  
Sudhansu Shekhar Patra ◽  
Siddharth Swarup Rautaray ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Performance Enhancement ◽  
Model Performance ◽  
Deep Belief Network ◽  
Arrhythmia Detection ◽  
Belief Network ◽  
Ecg Signals ◽  
Increase In Accuracy ◽  
Simple Logistic ◽  
Electrocardiogram Ecg

Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple Logistic Regression (LR) classifier. We compare and evaluate the results of data feature enrichment against plain, non-enriched data based on the metrics of precision, recall, specificity, and F1-score and report the extent of increase in performance. Also, we compare the performance of the DBN-LR pipeline with a 1D convolution technique and find that the DBN-LR algorithm achieves a 5% and 10% increase in accuracy when compared to 1D convolution and no feature extraction using DBN respectively.

Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

2019 ◽  
Vol 28 (3S) ◽  
pp. 802-805 ◽  
Author(s):  
Marieke Pronk ◽  
Janine F. J. Meijerink ◽  
Sophia E. Kramer ◽  
Martijn W. Heymans ◽  
Jana Besser
Keyword(s):  
Help Seeking ◽  
Hearing Aid ◽  
Model Performance ◽  
Area Under The Curve ◽  
Secondary Data ◽  
Support Program ◽  
Evaluation Period ◽  
A Prospective Study ◽  
Study Participants ◽  
Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

Performance Enhancement, Creativity and Mental Training

2003 ◽  
Author(s):  
M. Bar-Eli ◽  
O. Lowengart ◽  
J. Goldberg ◽  
S. Epstein ◽  
R. D. Fosbury
Keyword(s):  
Performance Enhancement ◽  
Mental Training

Effective performance improvement of organic thin film transistors with multi-layer modifications

2020 ◽  
Vol 91 (3) ◽  
pp. 30201
Author(s):  
Hang Yu ◽  
Jianlin Zhou ◽  
Yuanyuan Hao ◽  
Yao Ni
Keyword(s):  
Thin Film ◽  
Thin Film Transistors ◽  
Performance Enhancement ◽  
Organic Thin Film Transistors ◽  
Electrical Performance ◽  
Hole Injection ◽  
Organic Thin Film ◽  
Effective Performance ◽  
Significant Performance ◽  
P Type

Organic thin film transistors (OTFTs) based on dioctylbenzothienobenzothiophene (C8BTBT) and copper (Cu) electrodes were fabricated. For improving the electrical performance of the original devices, the different modifications were attempted to insert in three different positions including semiconductor/electrode interface, semiconductor bulk inside and semiconductor/insulator interface. In detail, 4,4′,4′′-tris[3-methylpheny(phenyl)amino] triphenylamine (m-MTDATA) was applied between C8BTBTand Cu electrodes as hole injection layer (HIL). Moreover, the fluorinated copper phthalo-cyanine (F16CuPc) was inserted in C8BTBT/SiO2 interface to form F16CuPc/C8BTBT heterojunction or C8BTBT bulk to form C8BTBT/F16CuPc/C8BTBT sandwich configuration. Our experiment shows that, the sandwich structured OTFTs have a significant performance enhancement when appropriate thickness modification is chosen, comparing with original C8BTBT devices. Then, even the low work function metal Cu was applied, a normal p-type operate-mode C8BTBT-OTFT with mobility as high as 2.56 cm2/Vs has been fabricated.

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